R中的轨道回归模型中的交互作用项和随机效应

Question

Can anyone tell me if it is possible to incorporate:
a)an interaction term
b)a random effect in a Tobit regression model in R?

For the interaction term I have been working on the following script, but that doesn't work.

fit <- vglm(GAG_p_DNA~factor(condition)+factor(time)+factor(condition):factor(time), 
      tobit(Lower = 0))
Error in if ((temp <- sum(wz[, 1:M, drop = FALSE] < wzepsilon))) warning(paste(temp,  : 
argument is not interpretable as logical

I have also tried this with dummy variables, created in the following way:

time.ch<- C(time, helmert,2)
print(attributes(time.ch))
condition.ch<-C(condition, helmert, 3)
print(attributes(condition.ch))

but I get the same error.

Part of the dataset (GAG_p_DNA values of zero are left censored) (Warning: to those who may be copying this. The OP used tabs as separators.)

Donor Time Condition GAG_p_DNA cens_GAG_p_DNA
1   1   6   0.97    1
1   1   10  0.93    1
1   7   2   16.65   1
1   7   6   0.94    1
1   7   10  1.86    1
1   28  2   21.66   1
1   28  6   0.07    1
1   28  10  3.48    1
2   1   1   1.16    1
2   1   2   2.25    1
2   1   6   2.41    1
2   1   10  1.88    1
2   7   2   13.19   1
2   7   10  2.54    1
2   28  2   23.93   1
2   28  6   0   0
2   28  10  15.17   1

I most likely need to use a Tobit regression model, as it seems that a Cox model with left censored data is not supported by R...

fit<- survfit(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left")~factor(condition)+factor(Time))] [Error in coxph(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ factor(condition) + : Cox model doesn't support "left" survival data

Answer 1

Try this:

survreg(Surv( GAG_p_DNA, cens_GAG_p_DNA, type='left') ~ 
                factor(Time)*factor(Condition), data=sdat, dist='gaussian')

(Recommended by Therneau: http://markmail.org/search/?q=list%3Aorg.r-project.r-help+therneau+left+censor+tobit#query:list%3Aorg.r-project.r-help%20therneau%20left%20censor%20tobit+page:1+mid:fnczjvrnjlx5jsp5+state:results )

--- earlier effort;

With that tiny dataset (where I have corrected the use of tabs as separators) you won't get much. I corrected two errors (spelling of "Condition" and using 0 for left censoring where it should be 2 and it runs without error:

sdat$cens_GAG_p_DNA[sdat$cens_GAG_p_DNA==0] <- 2

fit <- survfit(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left") ~ 
                          factor(Condition) + factor(Time), data=sdat)
Warning messages:
1: In min(jtimes) : no non-missing arguments to min; returning Inf
2: In min(jtimes) : no non-missing arguments to min; returning Inf
3: In min(jtimes) : no non-missing arguments to min; returning Inf
4: In min(jtimes) : no non-missing arguments to min; returning Inf
5: In min(jtimes) : no non-missing arguments to min; returning Inf
6: In min(jtimes) : no non-missing arguments to min; returning Inf
7: In min(jtimes) : no non-missing arguments to min; returning Inf
8: In min(jtimes) : no non-missing arguments to min; returning Inf
9: In min(jtimes) : no non-missing arguments to min; returning Inf
> fit
Call: survfit(formula = Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ 
    factor(Condition) + factor(Time), data = sdat)

                                      records n.max n.start events median
factor(Condition)=1, factor(Time)=1         1     2       2      0   1.16
factor(Condition)=2, factor(Time)=1         1     2       2      0   2.25
factor(Condition)=2, factor(Time)=7         2     3       3      0  14.92
factor(Condition)=2, factor(Time)=28        2     3       3      0  22.80
factor(Condition)=6, factor(Time)=1         2     3       3      0   1.69
factor(Condition)=6, factor(Time)=7         1     2       2      0   0.94
factor(Condition)=6, factor(Time)=28        2     2       2      2   0.00
factor(Condition)=10, factor(Time)=1        2     3       3      0   1.41
factor(Condition)=10, factor(Time)=7        2     3       3      0   2.20
factor(Condition)=10, factor(Time)=28       2     3       3      0   9.32
                                      0.95LCL 0.95UCL
factor(Condition)=1, factor(Time)=1        NA      NA
factor(Condition)=2, factor(Time)=1        NA      NA
factor(Condition)=2, factor(Time)=7     13.19      NA
factor(Condition)=2, factor(Time)=28    21.66      NA
factor(Condition)=6, factor(Time)=1      0.97      NA
factor(Condition)=6, factor(Time)=7        NA      NA
factor(Condition)=6, factor(Time)=28     0.00      NA
factor(Condition)=10, factor(Time)=1     0.93      NA
factor(Condition)=10, factor(Time)=7     1.86      NA
factor(Condition)=10, factor(Time)=28    3.48      NA

The other aspect which I would call an error as well would be not using a data argument to regression functions. Trying to use "attached" dataframes, with any regression function but especially with the 'survival' package, will often cause strange errors.

I did find that putting in an interaction by way of hte formula method generated this error:

Error in survfit.formula(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~  : 
  Interaction terms are not valid for this function

And I also found that coxme::coxme, which I had speculated might give you access to mixed effects, did not handle left censoring.

fit <- coxme(Surv(GAG_p_DNA, cens_GAG_p_DNA, type="left")~factor(Condition)*factor(Time), data=sdat)
Error in coxme(Surv(GAG_p_DNA, cens_GAG_p_DNA, type = "left") ~ factor(Condition) *  : 
  Cox model doesn't support 'left' survival data